Probiotics and Antibiotic-Induced Microbial Aberrations in Children

This randomized clinical trial assesses the effect of a multispecies probiotic supplement on antibiotic-associated diarrhea among patients aged 18 years and younger who are receiving antibiotics.


Introduction
Antibiotics are among the most frequently prescribed drugs in children. 1,2Currently, antibiotic prescription rates range from 0.5 to 1.6 courses per child-year in Western countries. 3Exposure to antibiotics results in a decreased diversity and abundance of commensal microorganisms with a concurrent increase of pathogens in the gut microbiota. 4,5In early life, gut microbiota play an important role in multiple physiologic processes, including priming and development of the immune system and digestion. 6][9][10][11] In the short term, the most common adverse effect is antibioticassociated diarrhea (AAD). 4Since prescription of antibiotics cannot always be avoided, it is pivotal to study interventions that could prevent, mitigate, or quickly restore antibiotic-induced microbial alterations and adverse events in children.
The most thoroughly studied intervention to prevent harms of antibiotic use consists of probiotics, defined as "live microorganisms that, when administered in adequate amounts, confer a health benefit on the host." 12 Recently, we demonstrated in a randomized clinical trial (RCT) that supplementation of a multispecies probiotic in antibiotic-exposed children resulted in a significantly decreased risk of diarrhea. 13It has been hypothesized that concomitant supplementation of probiotics during antibiotic therapy may protect against such antibiotic-induced adverse events by modulating the microbiota. 14,15However, the presumed underlying protective mechanisms of probiotics, including their mitigating effects on antibiotic-induced microbiota aberrations, have not yet been thoroughly studied in children. 14Therefore, we longitudinally assessed the effect of a multispecies probiotic on the microbiota composition in children receiving antibiotics.

Methods
This secondary analysis of an RCT followed the Consolidated Standards of Reporting Trials (CONSORT) reporting guideline.The study was approved by the Bioethics Committees of the Medical University of Warsaw, Warsaw, Poland, and Amsterdam University Medical Center, Amsterdam, the Netherlands.Written informed consent was obtained from all parents for children aged 0 to 11 years, children and parents for children aged 12 to 15 years, and children only for those 16 years or older.

Study Design
We conducted a quadruple-blind, placebo-controlled RCT in 3 Dutch and 2 Polish hospitals. 16The primary aim of the trial was to assess the effect of a multispecies probiotic on the incidence of AAD, for which results were reported previously. 13This RCT compared a placebo group with a probiotic supplement group.We obtained fecal samples from children in the RCT to longitudinally describe the effects of a multispecies probiotic on the gastrointestinal tract microbiota composition in children receiving antibiotics.The trial protocol is provided in Supplement 1.

Participants
All children and adolescents aged 3 months to 18 years (hereinafter referred to as children) starting broad-spectrum oral or intravenous antibiotic treatment were eligible for participation.Children were recruited from February

Procedures and Interventions
Children received either a multispecies probiotic or placebo twice a day for the duration of antibiotic treatment and the 7 subsequent days, up to a maximum of 17 days, starting within 24 hours of the first antibiotic dose.Randomization and masking procedures have been described previously. 13The multispecies probiotic (Ecologic AAD 612; Winclove Probiotics BV) contained 8 bacterial strains: Bifidobacterium bifidum W23, Bifidobacterium lactis W51, Lactobacillus acidophilus W37, L acidophilus

Sample Handling
Samples were analyzed in the Laboratory of Wageningen University & Research, Wageningen, the Netherlands, using procedures described previously. 17A total of 250 μg of each fecal sample was homogenized using bead beating, and DNA was extracted with a commercially available system (Maxwell 16; Promega Corporation) according to the manufacturer's protocol.Polymerase chain reaction was performed to amplify the V4 hypervariable regions of the bacterial 16S ribosomal RNA (rRNA) gene using barcoded primers 515F (5′-GTGCCAGCMGCCGCGGTAA-) and 806R (5′-GTGCCAGCMGCCGCGGTAA-). Six libraries were constructed by pooling 70 uniquely barcoded samples per library.Quality control was assessed by adding negative controls and artificial mock communities to libraries.Amplicon mixture was sequenced using a commercially available platform (HiSeq 2000; Illumina, Inc).Data processing used a semantic framework (NG-Tax [open source]) with default settings, apart from read length, which was set to 100 base pairs. 18Taxonomic assignment of amplicon sequence variants (ASVs) was performed using a reference database (SILVA_138.1). 19,20All laboratory analyses were performed by researchers unaware of participants' allocation to the probiotic intervention or control group.

Outcomes
The primary outcome of the original trial was the incidence of AAD, the results of which have been published previously. 13The effects of probiotics on the gastrointestinal microbiota composition in children receiving antibiotics was a secondary outcome of this trial, the data for which are presented in the present study.The objective was to analyze differences in changes of microbiota composition between the placebo and probiotic group over time.This was followed by cross-sectional comparison between the 2 groups at all time points separately.

Microbiome Data Analysis
All analyses were performed in R software, version 4.2.1 (R Project for Statistical Computing) using the microbiome, phyloseq, 21 and vegan 22 packages.All samples with read counts lower than those for the negative controls were excluded from further analysis.Taxa not assigned to any phylum were removed from the dataset.To ensure that the contaminant and rare taxa were removed, only taxa with abundance over 0.25% in at least 1 sample were left in the dataset. 23The median number of reads per sample for the 16S rRNA gene amplicon dataset was 175 933 (range, 2273-2 106 395).In total, 1471 different ASVs and 180 genera were identified.

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To quantify the breadth of individual microbiome diversity, we calculated alpha diversity (Shannon and inverse Simpson) indices for each sample on ASV level prior to filtering out rare taxa.
To analyze microbiota community changes over time, beta diversity was assessed separately for each of the study groups using the principal coordinates analysis method with Bray-Curtis distance on ASV taxonomic level.Moreover, to analyze differences in microbial beta diversity between groups, the analyses were performed separately for each collection time.All analyses of gut microbiota composition were performed based on the relative abundances of the taxa.

Statistical Analysis
Data were analyzed between September 1, 2022, and February 28, 2023.Descriptive statistics were used to present the participants' baseline characteristics of the 2 groups.For dichotomous data, the χ 2 test was used.For continuous data, the unpaired 2-tailed t test and Mann-Whitney test were used for normally and nonnormally distributed data, respectively.All statistical tests were 2 tailed and were performed with a 5% level of significance; P < .05 was considered statistically significant.First, changes in diversity and abundance over time were compared, followed by cross-sectional comparison at the 4 times separately.
To assess changes of alpha diversity over time in antibiotic-exposed children with or without probiotic supplementation, linear mixed models adjusted for age and country were used (lme4 package in R, version 4.2.1).To account for repeated measurements, participants' identifications were used as random effects.Evaluation of the statistical significance of the time term in the model was assessed by comparing (using χ 2 statistics) the built model with the model where time was dropped.In case of significant results, a post hoc Tukey test was used (emmeans package in R, version 4.2.1).All P values were corrected using the false discovery rate approach, and P < .05 was considered statistically significant.Next, statistical significance of differences in the compositional change trajectories between groups was assessed by testing the interaction effect between time point and group coding using linear mixed models with participants' identifications as random effects.The analysis was adjusted for age and country.Permutational analysis of variance (PERMANOVA) was used to test whether the bacterial composition was related to study group and time and whether there was an interaction between time and study group.Then, to assess which times differed significantly from one another, linear mixed-effects models were used in which the distances between points on the coordinate axes were compared between times within each group.
Moreover, overall changes in beta diversity were studied by calculating dissimilarity indices.This was done by comparing the Bray-Curtis distance between samples of the same participants collected in times 1 and 2, 1 and 3, and 1 and 4. Dissimilarity indices were then compared between the groups, and linear mixed-model effects were used to check whether these changes are differentiated by group and time change interaction.
To compare the differences in microbiota composition changes between study groups, fold changes in each group between time 1 and times 2, 3, and 4 were calculated by dividing the relative abundance of the taxa in a later time by the relative abundance in an earlier time.Since microbiome data are zero inflated, a zero replacement strategy was applied prior to fold changes calculation.All zeros were replaced by a constant value that was equal to 65% of the detection limit. 24A binary logarithm was then calculated for fold changes, and these values were compared between groups within each time change (from time 1 to 2, 1 to 3, and 1 to 4).The plots were prepared using the ggplot2 and microViz packages (R, version 4.2.1).

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Probiotics and Antibiotic-Induced Microbial Aberrations in Children
Beta diversity analysis showed that time was associated with overall microbiome composition in the placebo group (R 2 = 1.76%;P = .004)(Figure 3A).In the multidimensional space, samples from   3B) on the first axis.The dispersion of samples from each time in the probiotic group was not equal (mean difference in distance to centroid between times 2 and 4, −0.05 [95% CI, −0.09 to −0.01; P = .008];between times 3 and 4, −0.04 [95% CI, −0.08 to 0.00; P = .05]for homogeneity of variance test); therefore, the association of overall microbiome composition with time could not be assessed in this group.However, PERMANOVA analysis indicated there was no interaction effect between groups and time (F = 0.56; P = .97),indicating there were no significant differences between groups in beta diversity changes over time.
Dissimilarity indices also showed that microbiota composition in time 4 was dissimilar compared with the microbiota composition in time 1 in both groups.The composition in times 2 and 3 were equally dissimilar from time 1 as well (Figure 3C).Cross-sectional analysis of the beta diversity showed no difference between the placebo and probiotic groups at all 4 times (eFigure 1 in Supplement 2).b Percentages do not add to 100 because some participants were given a combination of antibiotics.
c Diarrhea was defined as 3 or more loose or watery stools in a 24-hour period regardless of the etiology.
d AAD was defined as 3 or more loose or watery stools in a 24-hour period, caused either by Clostridium difficile or of otherwise unexplained etiology, after testing for common, predefined diarrheal pathogens.

Differences in the Changes of Taxonomic Composition Between the Placebo and Probiotic Groups
Compared with time 1, there was a decrease at time 2 in the abundance of the genus Eubacterium in the placebo group (mean drop of 0.03% [95% CI, −0.10% to 0.04%]), whereas no changes were observed in the probiotic group (0 [95% CI, 0-0]; P = .05for the comparison of log fold changes between groups).There were no significant differences between the study groups in changes of other genera in time 2 compared with time 1.At time 3, there were significant differences between the placebo and probiotic groups in the change of relative abundance of 4 genera compared with time 1.The increase in Ligilactobacillus species was significantly greater in the probiotic group (0.16% [95% CI, −0.05% to 0.37%]) compared with the placebo group (0 [95% CI, 0-0]; P = .02).

Placebo group Probiotic group
Trajectory of alpha diversity changes in time differed between groups since the interaction term between group and time was statistically significant in linear mixed models explaining the Shannon index and the inverse Simpson index.A, Mean (SD) Shannon indices for the placebo compared with probiotic groups.B, Mean (SD) inverse Simpson indices for the placebo compared with probiotic groups.Both diversity indices were higher at time 4 (1-month follow-up) compared with time 1 (first sample after inclusion) and time 2 (last day of antibiotic treatment) in the placebo group.Boxes indicate upper and lower quartiles; horizontal lines in boxes, median; and whiskers, minimum and maximum.).These reported P values along with the reported increase or decrease refer to the comparison between differences in log fold changes between the placebo and probiotic groups as shown in Figure 4.

Cross-Sectional Differences in Taxonomic Composition Between the Placebo and Probiotic Groups
Regarding the taxonomic composition of the microbiota at the phylum level, Verrucomicrobiota had a higher relative abundance in the placebo group compared with the probiotic group at time 4 (mean  A, Overall microbiome composition was associated with time in the placebo group (R 2 = 1.76%;P = .004)but not in the probiotic group (P = .08).B, Samples from time 4 were significantly further from samples in times 1 (P = .001), 2 (P < .001),and 3 (P = .02)on the first axis in the placebo group.Samples from time 2 were significantly further from samples in times 1 (P = .04),3 (P = .005),and 4 (P = .04)on the second axis in the placebo group.C, The dispersion of samples from each time point in the probiotic group was not equal, therefore the association of overall microbiome composition with time could not be assessed in this group (time effect P > .99 for the probiotic group and P = .70for the placebo group; P = .53for interaction in the probiotic group).Boxes indicate upper and lower quartiles; horizontal lines in boxes, median; and whiskers, minimum and maximum.
genera included in the placebo and probiotic groups is given in eTables 2 to 4 in Supplement 3 along with adjusted P values.

Discussion
In this secondary analysis of an RCT, we investigated the effect of probiotic supplementation on antibiotic-associated microbiota aberrations in children.Alpha diversity did not differ between the 2 groups during the intervention period, but the Shannon diversity and inverse Simpson indices were higher in the placebo group 1 month after cessation of the intervention.The studied probiotics had minor and transient effects on the microbiota, including increased abundance of 3 of 5 genera during supplementation.
It is hypothesized that probiotics mitigate antibiotic-induced gut microbiota aberrations and consequently decrease antibiotic-related adverse effects such as AAD.However, mechanistic evidence is limited, particularly in children. 14,15In this study, we did not observe major effects of antibiotics on diversity indices in either of the groups, in contrast to what was expected and to previous studies in children. 4,25This may be due to most of the baseline stool samples being collected after ingestion of 1 or more dose of antibiotics, since it was not feasible or ethical to postpone start of antibiotic therapy until after the first stool sampling.The first antibiotic doses may consequently have already affected the microbiota composition measured in the baseline sample, as supported by the differences in 5 genera between groups at time 1.One may therefore speculate that the alpha diversity at baseline, before starting antibiotic therapy, was in reality higher than measured in our baseline samples.If that indeed was the case, the alpha diversity would first decrease during antibiotic treatment (time 2).Then, in the placebo group, the alpha diversity would increase or return to baseline at time 4, and not in the probiotic group.A 2018 study 26 concluded that

Figure 4 . 1 Source
Figure 4. Log Fold Changes Compared With Time 1 eFigure

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1, 2018, to May 31, 2021.Children were eligible if recruited within 24 JAMA Network Open.2024;7(7):e2418129.doi:10.1001/jamanetworkopen.2024.18129(Reprinted) July 5, 2024 2/14 13ildren were only included in the microbiota analysis if the child or parents collected 2 or more fecal samples and if children were adherent with the study protocol.Children were considered adherent if they received over 75% of the recommended doses of the study product.Exclusion criteria have been described previously.13 Downloaded from jamanetwork.comby guest on 07/07/2024 hours following initiation of antibiotics.

in Microbial Diversity Between the Placebo and Probiotic Groups No
the 350 children included in the original RCT, 88 (44 in the probiotic group and 44 in the placebo group; 54 boys and 34 girls; mean [SD] age, 47.09 [55.64] months) were adherent to the study protocol regarding collection of at least 2 stool samples with enough reads between February 1, 2018, and May 31, 2021 (Figure1).A total of 19 samples had to be excluded from the analyses because of low read counts.Participants' characteristics were comparable between the 2 groups (Table).Characteristics of participants included in this study and participants who dropped out after inclusion in the original trial are provided in eTable 1 in Supplement 2. Race and ethnicity data were not collected.A limited number of children in each group (13 in the placebo group [29.5%] and 7 in the probiotic group [15.9%]) had diarrhea, with 10 in the placebo group (22.7%) and 6 in the probiotic group (13.6%) with AAD.differences were found in change of any of the alpha diversity indices during the first 2 collection times in both study groups.In the placebo group, higher values were found at time 4 compared with time 2 in the Shannon diversity index (mean [SD], 3.56 [0.75] vs 3.09 [1.00]; P = .02)andtheinverse Simpson index (mean [SD], 3.75 [95% CI, 1.66-5.82]vs−1.31[95% CI, −3.17 to 0.53]; P < .001)(Figure2).Such changes across times were not noticed in the probiotic group, and regression analysis showed that the study groups differed in the trajectories of changes in both the Shannon index (β coefficients, −0.22 [95% CI, −0.56 to 0.12] at time 1; −0.39 [95% CI, −0.74 to −0.04] at time 2; −0.09 [95% CI, −0.44 to 0.26] at time 3; and 0.14 [95% CI, −0.23 to 0.51] at time 4; P = .05for interaction) and inverse Simpson index (β coefficients, −2.57[95% CI, −5.94 to 0.81] for time 1; −3.55 [95% CI, −6.99 to −0.09] for time 2; 0.28 [95% CI, −3.17 to 3.75] for time 3; and 3.72 [95% CI, 0.10-7.34]for time 4; P < .001for interaction).Cross-sectional comparison between the placebo and probiotic groups revealed no differences in Shannon diversity and inverse Simpson indices in the first 3 times.The Shannon diversity index was higher in the placebo group compared with the probiotic group at time 4 (mean [SD], 3.56 [0.75] vs 3.25 [0.83]; P = .048)(Figure 2A), as was the inverse Simpson index (17.92[10.08] vs 12.52 [9.00]; P = .03)(Figure

Table .
Participant Characteristics a a Unless otherwise indicated, data are expressed as No. (%) of patients.
Probiotics and Antibiotic-Induced Microbial Aberrations in Children P = .004)at time 3.No differences in Bifidobacteria were found between the 2 groups at any of the 4 times.No significant differences were found in the supplemented genera at time 4, which corresponds to 1 month after cessation of intake of the study material.An overview of all observed 2. Relative Abundance at Phylum Level eFigure 3. Relative Abundance of Genera With Significantly Different Relative Abundance